Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A biosensor, comprising: a photoplethysmography (PPG) circuit configured to obtain at least a first PPG signal from light reflected from or transmitted through skin tissue of a patient, wherein the light includes a first wavelength in a range of 380 nm to 410 nm; and a processing circuit configured to: obtain a measurement of nitric oxide (NO) level in blood flow using at least the first PPG signal; determine an infection present in the patient using the measurement of the NO level in blood flow; and generate an indication of the infection.
2. The biosensor of claim 1 , wherein the processing circuit is further configured to generate a severity level of the infection in the patient.
3. The biosensor of claim 1 , wherein the processing circuit is further configured to determine a heart rate and respiration of the patient using the first PPG signal and generate the indication of the infection in the patient using the measurement of NO and the heart rate and the respiration rate.
4. The biosensor of claim 1 , further comprising: a temperature sensor configured to measure a skin temperature of the patient; and wherein the processing circuit is further configured to determine the infection in the patient using the measurement of NO and the skin temperature of the patient.
5. The biosensor of claim 1 , wherein the optical circuit is configured to obtain a plurality of PPG signals at a plurality of different wavelengths reflected from tissue of a user, wherein the plurality of different wavelengths have varying penetration depths of tissue, wherein one of the plurality of PPG signals is the first PPG signal.
6. The biosensor of claim 5 , wherein the processing circuit is further configured to: determine a plurality of L values using the plurality of PPG signals, wherein each of the plurality of L values is determined by isolating an alternating current (AC) component of a different one of the plurality of PPG signals; determine a plurality of R values using the plurality of L values, wherein each of the plurality of R values is determined using a ratio of two L values of the plurality of L values; and determine the infection in the patient using the plurality of L values and the plurality of R values.
A biosensor system is designed to detect infections in a patient by analyzing photoplethysmographic (PPG) signals. The system addresses the challenge of accurately identifying infections, which often require invasive or time-consuming diagnostic methods. The biosensor includes a processing circuit that processes multiple PPG signals obtained from the patient. The processing circuit isolates the alternating current (AC) component from each PPG signal to generate a set of L values, where each L value corresponds to a different PPG signal. These L values are then used to compute a set of R values, each derived from the ratio of two L values. The system determines the presence or severity of an infection by analyzing the combined data from the L values and R values. This approach leverages the physiological changes in PPG signals associated with infections, providing a non-invasive and rapid diagnostic tool. The method enhances diagnostic accuracy by utilizing multiple signal components and their relationships, improving early detection and monitoring of infections.
7. The biosensor of claim 6 wherein the processing circuit is further configured to: determine one or more other PPG parameters using the plurality of PPG signals; determine the infection in the patient using the plurality of L values, the plurality of R values and the one or more other PPG parameters.
8. The biosensor of claim 7 , wherein the one or more other PPG parameters include at least one of: a phase delay between the first PPG signal and a second PPG signal of the plurality of additional PPG signals, a correlation of phase shape between the first PPG signal and the second PPG signal or a periodicity of first PPG signal or the second PPG signal.
9. The biosensor of claim 7 , wherein the processing circuit is configured to determine a confidence level in the indication of infection in the patient using the plurality of L values, the plurality of R values and the one or more other PPG parameters.
10. The biosensor of claim 7 , wherein the processing circuit is further configured to determine a type of the infection using the plurality of L values, the plurality of R values and the one or more other PPG parameters, wherein the type of infection includes at least one of: sepsis, COVID-19, pneumonia, or influenza.
11. The biosensor of claim 7 , wherein the plurality of L values includes: a first L value determined using the first PPG signal obtained at the first wavelength in a range of 380 nm-410 nm; and a second L value determined using a second PPG signal of the plurality of PPG signals, wherein the second PPG signal is obtained at a second wavelength equal to or above 660 nm.
This invention relates to a biosensor system for measuring physiological parameters using photoplethysmography (PPG) signals at multiple wavelengths. The problem addressed is improving the accuracy and reliability of biosensor measurements by leveraging PPG signals obtained at specific wavelength ranges. The biosensor includes a light source configured to emit light at multiple wavelengths and a detector to capture PPG signals from a user's tissue. The system processes these signals to determine a plurality of pathlength (L) values, which are used to calculate physiological metrics such as blood oxygen saturation (SpO2) or other biochemical parameters. The biosensor specifically uses a first PPG signal obtained at a first wavelength in the range of 380 nm to 410 nm to determine a first L value. Additionally, it uses a second PPG signal obtained at a second wavelength equal to or above 660 nm to determine a second L value. These wavelength ranges are selected to enhance signal quality and accuracy by optimizing light absorption characteristics of different tissue components, such as hemoglobin and other chromophores. The combination of these L values improves the biosensor's ability to compensate for variations in tissue properties and environmental factors, leading to more precise measurements. The system may also include additional processing steps to filter noise, normalize signals, or apply calibration algorithms to further refine the results.
12. The biosensor of claim 7 , wherein the plurality of R values includes: an R value determined using the first PPG signal obtained at the first wavelength in a range of 380 nm-410 nm and the second PPG signal obtained at the second wavelength equal to or above 660 nm; an R value determined using the first PPG signal obtained at the first wavelength in the range of 380 nm-410 nm and a third PPG signal of the plurality of additional PPG signals, wherein the third PPG signal is obtained at a third wavelength in a range of 510 nm-550 nm; or an R value determined using the third PPG signal obtained at the third wavelength in the range of 510 nm-550 nm and the second PPG signal obtained at the second wavelength equal to or above 660 nm.
13. The biosensor of claim 7 , wherein the processing circuit includes a neural network processing circuit, wherein the neural network processing device is pre-configured with a learning vector generated from a training set, wherein the training set includes the plurality of L values, the plurality of R values and the one or more other PPG parameters obtained from a plurality of patients diagnosed with the infection.
This invention relates to a biosensor system designed to detect infections by analyzing photoplethysmography (PPG) signals. The biosensor measures PPG signals from a patient, extracting key parameters such as left (L) and right (R) signal values, as well as additional PPG-derived metrics. These parameters are processed by a neural network circuit pre-trained on a dataset from patients diagnosed with infections. The neural network uses a learning vector derived from this training set to evaluate the PPG data and determine the likelihood of an infection. The system leverages machine learning to improve diagnostic accuracy by identifying patterns in PPG signals that correlate with infectious conditions. The neural network is pre-configured, meaning it does not require real-time training but instead applies learned patterns to new data for rapid infection detection. This approach enhances the biosensor's ability to provide early and reliable infection diagnostics, reducing reliance on traditional laboratory tests. The system integrates hardware and software components to process PPG signals efficiently, ensuring timely and accurate infection assessment.
14. A biosensor, comprising: an optical circuit configured to obtain at least a first PPG signal from light reflected from skin tissue of a patient, wherein the light includes a first wavelength in an ultraviolet (UV) range and at least a second PPG signal from light reflected from skin tissue of the patient, wherein the light includes a second wavelength in an infrared (IR) range; and one or more processing circuits configured to: obtain a measurement of nitric oxide (NO) levels in blood flow using the first PPG signal and the second PPG signal, wherein the measurement of NO levels is an R value determined using a ratio of an AC component of the first PPG signal and an AC component of the second PPG signal; generate an indication of infection in the patient using at least the measurement of NO levels; and generate a severity level of the infection using at least the measurement of NO levels.
15. The biosensor of claim 14 , wherein the one or more processing circuits is further configured to: determine a respiratory rate from the first or second PPG signals; determine an estimation of blood pressure from the first or second PPG signals; and determine a hybrid quick Sequential Organ Failure Assessment (qSOFA) score using the respiratory rate, the measurement of NO levels and the estimation of blood pressure.
16. The biosensor of claim 14 , wherein the one or more processing circuits is further configured to: determine a heart rate and a respiration rate of the patient using one or more of the first PPG signal or the second PPG signal and generate the indication of the risk of infection in the patient using the measurement of NO and the heart rate and the respiration rate.
A biosensor system is designed to monitor physiological parameters and assess infection risk in a patient. The system includes a photoplethysmography (PPG) sensor that captures first and second PPG signals from the patient, which may be derived from different wavelengths or sensor locations. The biosensor also measures nitric oxide (NO) levels in the patient. Processing circuits analyze these signals to determine the patient's heart rate and respiration rate, utilizing either or both PPG signals. The system then generates an indication of infection risk by combining the NO measurement with the derived heart rate and respiration rate. This integrated approach allows for a more comprehensive assessment of the patient's physiological state, particularly in detecting early signs of infection. The biosensor may be part of a wearable or portable device, enabling continuous or periodic monitoring in clinical or home settings. The technology addresses the need for non-invasive, real-time infection risk assessment, which is critical for early intervention and patient management.
17. The biosensor of claim 16 , further comprising: a temperature sensor configured to measure a skin temperature of the patient; and wherein the one or more processing circuits is further configured to generate the indication of the risk of infection in the patient using the measurement of NO, the heart rate, the respiration rate and the skin temperature of the patient.
A biosensor system is designed to monitor a patient's physiological parameters to assess the risk of infection. The system includes a nitric oxide (NO) sensor to measure NO levels in the patient's breath, a photoplethysmography (PPG) sensor to detect heart rate and respiration rate, and a temperature sensor to measure skin temperature. The biosensor processes these measurements using one or more processing circuits to generate an indication of infection risk. The system combines NO levels, heart rate, respiration rate, and skin temperature to provide a comprehensive assessment. The integration of multiple physiological parameters enhances the accuracy of infection risk detection, enabling early intervention. The biosensor is particularly useful in clinical settings where continuous monitoring of infection risk is critical. The system may be worn by the patient or integrated into medical devices for real-time monitoring. The combination of these sensors allows for a multi-faceted approach to infection risk assessment, improving diagnostic reliability.
18. The biosensor of claim 14 , wherein the one or more processing circuits is further configured to: determine the R value using the first PPG signal obtained at the first wavelength in a range of 380 nm-400 nm and the second PPG signal obtained at the second wavelength equal to or above 660 nm; and generate the indication of the infection in the patient using the R value.
This invention relates to a biosensor system for detecting infections in a patient by analyzing photoplethysmographic (PPG) signals at specific wavelengths. The problem addressed is the need for a non-invasive, rapid method to detect infections, such as sepsis, by leveraging optical sensing techniques. The biosensor includes a light source configured to emit light at two distinct wavelengths: a first wavelength in the range of 380 nm to 400 nm and a second wavelength equal to or above 660 nm. The system also includes a detector to measure the reflected or transmitted light, generating PPG signals at these wavelengths. One or more processing circuits analyze these signals to compute an R value, which is derived from the ratio or difference between the first and second PPG signals. The R value is then used to generate an indication of infection in the patient, such as sepsis, based on predefined thresholds or patterns associated with infectious states. The biosensor may also include additional components, such as a display for presenting the infection indication or a communication interface for transmitting data to external devices. The system may further incorporate calibration mechanisms to ensure accurate signal measurement and processing. The invention aims to provide a portable, real-time diagnostic tool for early infection detection, improving patient outcomes by enabling timely medical intervention.
19. The biosensor of claim 18 , wherein the one or more processing circuits is further configured to: determine a second R value using a third PPG signal obtained at a third wavelength in the range of 510 nm and 550 nm and the second PPG signal obtained at the second wavelength equal to or above 660 nm, wherein the second R value is a measurement of creatinine in blood flow; and generate the indication of the infection in the patient using the first R value for the measurement of NO levels in blood flow and the second R value for the measurement of creatinine in blood flow.
20. The biosensor of claim 19 , wherein the one or more processing circuits is further configured to: determine a third R value using a fourth PPG signal obtained at a fourth wavelength in the range of 448 nm and 488 nm and the second PPG signal obtained at the second wavelength equal to or above 660 nm, wherein the third R value is a measurement of a liver enzyme P450 in blood flow; and generate the indication of the infection in the patient using the first R value for the measurement of NO levels in blood flow, the second R value for the measurement of creatinine in blood flow and the third R value for the measurement of the liver enzyme P450 in blood flow.
A biosensor system is designed to detect infections by analyzing blood flow parameters using photoplethysmography (PPG) signals at multiple wavelengths. The system addresses the challenge of early and accurate infection detection by measuring key biomarkers associated with infection, including nitric oxide (NO), creatinine, and liver enzyme P450. The biosensor obtains PPG signals at specific wavelengths: a first wavelength between 400 nm and 440 nm for NO measurement, a second wavelength at or above 660 nm for creatinine measurement, and a third wavelength between 448 nm and 488 nm for liver enzyme P450 measurement. The system calculates R values from these signals, where each R value represents the ratio of PPG signal amplitudes at different wavelengths, correlating to the respective biomarker levels. By combining these R values, the biosensor generates an indication of infection in a patient, leveraging the combined diagnostic value of NO, creatinine, and liver enzyme P450 as infection markers. This approach enables non-invasive, multi-biomarker infection detection, improving diagnostic accuracy and timeliness.
21. The biosensor of claim 14 , wherein the one or more processing circuits is further configured to: determine a heart rate and respiratory rate from the first or second PPG signals; determine an estimation of blood pressure from the first or second PPG signals; and determine a hybrid quick Sequential Organ Failure Assessment (qSOFA) score using the respiratory rate, the heart rate, the estimation of blood pressure and the R value.
A biosensor system is designed to monitor physiological parameters for early detection of sepsis or other critical conditions. The system captures photoplethysmographic (PPG) signals from a patient using one or more light sources and detectors. The biosensor processes these signals to derive heart rate and respiratory rate, as well as an estimated blood pressure. Additionally, the system calculates a hybrid quick Sequential Organ Failure Assessment (qSOFA) score by combining the respiratory rate, heart rate, blood pressure estimate, and a derived R value, which represents a ratio of signal components. The hybrid qSOFA score integrates these parameters to provide a more comprehensive assessment of a patient's condition, improving early detection of sepsis or other critical states. The biosensor may also include features such as adaptive filtering to enhance signal quality and reduce noise, ensuring accurate physiological measurements. This system enables continuous, non-invasive monitoring of vital signs, facilitating timely medical intervention.
22. A biosensor, comprising: an optical circuit configured to obtain a plurality of PPG signals from light at a plurality of different wavelengths reflected from skin tissue of a patient, wherein at least a first PPG signal is obtained from a first wavelength in a range of 380 nm to 410 nm and at least a second PPG signal is obtained from a second wavelength in a range of 920 nm to 960 nm; and one or more processing devices configured to: obtain a first R value using the first PPG signal and the second PPG signal; and generate an indication of a severity of an infection in the patient using at least the first R value.
23. The biosensor of claim 22 , wherein the one or more processing devices are further configured to: determine a heart rate using one or more of the plurality of PPG signals; and generate the indication of the severity of the infection in the patient using at least the first R value and the heart rate.
This invention relates to a biosensor system designed to assess infection severity in a patient by analyzing photoplethysmographic (PPG) signals. The biosensor includes a sensor module that captures multiple PPG signals from the patient, each representing different physiological parameters. A processing device analyzes these signals to extract a first R value, which is a metric derived from the PPG waveforms to indicate infection-related changes in the patient's vascular system. The system further determines the patient's heart rate from the PPG signals. By combining the first R value and the heart rate, the biosensor generates an indication of infection severity, providing a quantitative assessment that can aid in clinical decision-making. The integration of multiple PPG signals and physiological metrics enhances the accuracy and reliability of infection severity monitoring, addressing the need for non-invasive, real-time diagnostic tools in healthcare settings. This approach leverages existing biosensor technology to provide a more comprehensive evaluation of a patient's condition, improving early detection and management of infections.
24. The biosensor of claim 23 , wherein the one or more processing devices are further configured to: determine a second R value using a third PPG signal and the second PPG signal, wherein the third PPG signal is obtained from a third wavelength in a range of 510 nm to 550 nm; and generate the indication of the severity of the infection in the patient using at least the first R value, the second R value and the heart rate.
25. The biosensor of claim 24 , wherein the one or more processing devices are further configured to: determine a third R value using the third PPG signal and a fourth PPG signal, wherein the fourth PPG signal is obtained from a fourth wavelength at 660 nm; and generate the indication of the severity of the infection in the patient using at least the first R value, the second R value, the third R value and the heart rate.
This invention relates to a biosensor system for assessing infection severity in a patient by analyzing photoplethysmographic (PPG) signals at multiple wavelengths. The system addresses the challenge of accurately detecting and quantifying infection severity through non-invasive, real-time monitoring. The biosensor includes one or more processing devices configured to process PPG signals obtained from different wavelengths to derive specific metrics. A first R value is determined using a first PPG signal from a first wavelength and a second PPG signal from a second wavelength at 808 nm. A second R value is derived from the first PPG signal and a third PPG signal from a third wavelength at 940 nm. Additionally, a third R value is calculated using the third PPG signal and a fourth PPG signal from a fourth wavelength at 660 nm. The system then generates an indication of infection severity by combining these R values with the patient's heart rate. This multi-wavelength approach enhances the accuracy of infection assessment by leveraging distinct physiological responses captured at different wavelengths, providing a comprehensive evaluation of the patient's condition. The system enables early detection and continuous monitoring of infection progression, supporting timely medical intervention.
26. The biosensor of claim 25 , wherein the one or more processing devices are further configured to: determine a measurement of a time difference between the first PPG signal and the second PPG signal; and generate the indication of the severity of the infection in the patient using at least the first R value, the second R value, the third R value, the measurement of the time difference and the heart rate.
27. The biosensor of claim 26 , wherein the one or more processing devices are further configured to: determine a measurement of oxygen saturation, wherein the measurement includes a fourth R value determined from the fourth PPG signal obtained from the fourth wavelength at 660 nm and from the second PPG signal obtained from the second wavelength in the range of 920 nm to 960 nm; and generate the indication of the severity of the infection in the patient using at least the first R value, the second R value, the third R value, the fourth R value, the measurement of the time difference and the heart rate.
28. The biosensor of claim 27 , wherein the one or more processing devices includes at least one neural network processing device.
A biosensor system is designed to detect and analyze biological signals, such as those from the human body, for medical or health monitoring applications. The system addresses challenges in accurately interpreting complex biological data, which often requires advanced processing to extract meaningful insights. The biosensor includes one or more processing devices configured to analyze the detected signals. To enhance processing capabilities, the system incorporates at least one neural network processing device. Neural networks are particularly effective for handling large datasets and identifying patterns that may not be easily detectable through traditional methods. This integration allows the biosensor to improve accuracy, adapt to varying conditions, and provide more reliable health monitoring. The neural network processing device may be trained on specific biological data to optimize performance for particular applications, such as detecting anomalies in heart rhythms or glucose levels. By leveraging neural networks, the biosensor can offer more sophisticated and personalized health insights compared to conventional systems. The overall design aims to bridge the gap between raw biological data and actionable medical information, supporting better diagnostic and preventive healthcare solutions.
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April 13, 2021
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